System vendors and manufacturers frequently distribute “patches” or upgrades and recommend modifications to system configurations. Such modifications may be recommended in an attempt to rectify existing system faults or to bring systems in line with recommended practice. However, systems administrators are often reluctant to carry out such recommended modifications since they may lead to inadvertent destabilization of systems and could potentially introduce hitherto unknown problems to the systems.
Problems associated with the malfunctioning of computer software and hardware on a particular system are often dependent on the precise configuration of the software and hardware of that system. It is usually difficult to identify why a particular error occurs on one system when it does not occur on another. One way in which large organizations may address this issue is to ensure that all systems within the organization have identically configured software and hardware. However, this is an expensive solution and it is available only when the software and hardware requirements of all computer system users within the organization are similar.
A number of different techniques have been developed for remote management of system configuration and for the management of large numbers of systems. However, known methods and systems typically rely on either a manual comparison or on manually written scripts to carry out a comparison between reconfiguration requests and known acceptable and unacceptable configurations for a system. Such methods are inefficient and too laborious for dealing with comprehensive assessments of a large number of systems. Manual comparisons and manually written scripts are not adept at recognizing factors or patterns which result in certain configurations being acceptable or unacceptable in particular systems. Manually written scripts are usually laborious to maintain.
Some methods have been developed that are able to correlate system configurations with past system performance data, allowing them to make judgments about consequences of various configuration changes in terms of system performance. However, these methods require significant amounts of accurate performance data relating to the systems to be evaluated. Typically, such performance data is incomplete, unreliable, or simply unavailable. In these circumstances, maintenance efforts may be guided using configuration conformance data.
It would be desirable to have a method and system which assist maintenance efforts by appropriate use of configuration conformance data.